K-Mer Fingerprinting with RNN to predict MICs for K. pneumoniae

Antimicrobial Susceptibility Testing (AST) is necessary to properly identify if a pathogen is susceptible or resistant to a given antimicrobial agent. However, current methods utilizing Minimum Inhibitory Concentration (MIC) testing are slow, leading to delay in implementing effective antimicrobial therapy. Machine Learning (ML) offers a faster alternative. This paper proposes a novel way to predict MIC values by fingerprinting isolates using three different K-Mer count sizes, rather than using a single, large K-mer, of three, four, and five. Results from our models achieved an average F1 score of 0.75 in comparison to other ML models that achieved 0.67 and 0.58 F1 scores by previous approaches.